According to IDC, digital data will skyrocket up to 175 zettabytes, and the huge part of this data is images. Data scientists need to (pre) process these images before feeding them into any machine learning models. They have to do the important (and sometimes dirty) work before the fun part begins.

To process a large amount of data with efficiency and speed without compromising the results data scientists need to use image processing tools for machine learning and deep learning tasks.

In this article, I am going to list out the most useful image processing libraries in Python which are being used heavily in machine learning tasks.

1. OpenCV

OpenCV is an open-source library that was developed by Intel in the year 2000. It is mostly used in computer vision tasks such as object detection, face detection, face recognition, image segmentation, etc but also contains a lot of useful functions that you may need in ML.

Gray-scaling

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt

img = cv.imread('goku.jpeg')
gray_image = cv.cvtColor(img, cv.COLOR_BGR2GRAY)

fig, ax = plt.subplots(1, 2, figsize=(16, 8))
fig.tight_layout()

ax[0].imshow(cv.cvtColor(img, cv.COLOR_BGR2RGB))
ax[0].set_title("Original")

ax[1].imshow(cv.cvtColor(gray_image, cv.COLOR_BGR2RGB))
ax[1].set_title("Grayscale")
plt.show()

#computer vision #machine learning tools #machine-learning